Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
This work introduces an Adaptive RNN scheme for Neural-Network Quantum States (NQS). Instead of using a fixed model size, the hidden state dimension of the recurrent neural network is increased iteratively during training. This adaptive approach significantly reduces computational cost and training time while maintaining or improving accuracy across various quantum many-body models.